Deep Learning Technique for Gymnastics Movements Evaluation Based on Pose Estimation
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-62881-8_19
Reference21 articles.
1. Echeverria, J., Santos, O.C.: Toward modeling psychomotor performance in karate combats using computer vision pose estimation. Sensors 21 (2021). https://doi.org/10.3390/s21248378
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3. Li, J.H., Tian, L., Wang, H., An, Y., Wang, K., Yu, L.: Segmentation and recognition of basic and transitional activities for continuous physical human activity. IEEE Access 7, 42565–42576 (2019). https://doi.org/10.1109/ACCESS.2019.2905575
4. Bhuiyan, R.A., Ahmed, N., Amiruzzaman, M., Islam, M.R.: A robust feature extraction model for human activity characterization using 3-axis accelerometer and gyroscope data. Sensors (Switzerland) 20, 1–17 (2020). https://doi.org/10.3390/s20236990
5. Rasheed, J. (ed.): 2022 International Conference on Artificial Intelligence of Things (ICAIoT), p. 449. Institute of Electrical and Electronics Engineers (IEEE), Turkey (2023). https://doi.org/10.1109/ICAIoT57170.2022
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